update imagenet demo

python/examples/imagenet/image_classifcation_service.py

@@ -12,17 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle_serving_server.plugin_service import PluginService
+from paddle_serving_server.web_service import WebService
 import sys
 import cv2
 import base64
-from PIL import Image
-from StringIO import StringIO
 import numpy as np
-from image_server import start_serving
 
 
-class ImageService(PluginService):
+class ImageService(WebService):
     def set_param(self):
         self.image_mean = [0.485, 0.456, 0.406]
         self.image_std = [0.229, 0.224, 0.225]
@@ -115,5 +112,7 @@ class ImageService(PluginService):
         return res_feed, fetch
 
 
-image_service = ImageService(name="image", model=sys.argv[1], port=9291)
-image_service.start_service()
+image_service = ImageService(name="image")
+image_service.load_model_config(sys.argv[1])
+image_service.prepare_server(workdir=sys.argv[2], port=9393, device="cpu")
+image_service.run_server()

python/examples/imagenet/image_http_client.py

@@ -21,7 +21,7 @@ import os
 import numpy as np
 
 
-def predict(image_path):
+def predict(image_path, server):
     image = open(image_path).read()
     image = base64.b64encode(image)
 
@@ -30,20 +30,19 @@ def predict(image_path):
     req["fetch"] = ["score"]
 
     req = json.dumps(req)
-    url = "http://127.0.0.1:9291/image/prediction"
+    url = server
     headers = {"Content-Type": "application/json"}
     r = requests.post(url, data=req, headers=headers)
     if r.status_code == requests.codes.ok:
         score = r.json()["score"]
         score = np.array(score)
-        print("max score : {} class {}".format(np.max(score), np.argmax(score)))
+        print("picture {} max score : {} class {}".format(
+            image_path, np.max(score), np.argmax(score)))
     else:
         print("predict {} error".format(image_path))
 
 
 if __name__ == "__main__":
-    folder = "./to_longteng/n01440764"
-    file_list = os.listdir(folder)
-    for f in file_list:
-        image_path = folder + "/" + f
-        predict(image_path)
+    server = "http://127.0.0.1:9393/image/prediction"
+    image_path = "./data/n01440764_10026.JPEG"
+    predict(image_path, server)

python/examples/imagenet/image_server.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpSeqMaker
-from paddle_serving_server import Server
-
-
-def start_serving():
-    op_maker = OpMaker()
-
-    read_op = op_maker.create('general_reader')
-    general_infer_op = op_maker.create('general_infer')
-    general_response_op = op_maker.create('general_response')
-
-    op_seq_maker = OpSeqMaker()
-    op_seq_maker.add_op(read_op)
-    op_seq_maker.add_op(general_infer_op)
-    op_seq_maker.add_op(general_response_op)
-
-    server = Server()
-    server.set_op_sequence(op_seq_maker.get_op_sequence())
-    server.set_num_threads(24)
-
-    server.load_model_config(sys.argv[1])
-    port = int(sys.argv[2])
-    server.prepare_server(workdir="work_dir1", port=port, device="cpu")
-    server.run_server()
-
-
-if __name__ == "__main__":
-    start_serving()